---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- nthakur/mirage-gpt-4o-sft-instruct-llama-3
- nthakur/mirage-meta-llama-3-mistral-sft-instruct-meta-llama-tokenizer
library_name: peft
license: llama3
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Meta-Llama-3-8B-Instruct-mirage-all-teacher-instruct-llama-3-sft

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the nthakur/mirage-gpt-4o-sft-instruct-llama-3 and the nthakur/mirage-meta-llama-3-mistral-sft-instruct-meta-llama-tokenizer datasets.
It achieves the following results on the evaluation set:
- Loss: 0.2593

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3535        | 0.0412 | 200  | 0.3586          |
| 0.4117        | 0.0824 | 400  | 0.3371          |
| 0.3577        | 0.1236 | 600  | 0.3277          |
| 0.3594        | 0.1649 | 800  | 0.3194          |
| 0.3603        | 0.2061 | 1000 | 0.3096          |
| 0.3633        | 0.2473 | 1200 | 0.3063          |
| 0.3078        | 0.2885 | 1400 | 0.3000          |
| 0.3274        | 0.3297 | 1600 | 0.2948          |
| 0.3474        | 0.3709 | 1800 | 0.2925          |
| 0.3401        | 0.4122 | 2000 | 0.2875          |
| 0.3124        | 0.4534 | 2200 | 0.2839          |
| 0.3095        | 0.4946 | 2400 | 0.2802          |
| 0.3532        | 0.5358 | 2600 | 0.2775          |
| 0.301         | 0.5770 | 2800 | 0.2757          |
| 0.3204        | 0.6182 | 3000 | 0.2712          |
| 0.3158        | 0.6595 | 3200 | 0.2687          |
| 0.3032        | 0.7007 | 3400 | 0.2667          |
| 0.2851        | 0.7419 | 3600 | 0.2645          |
| 0.2903        | 0.7831 | 3800 | 0.2629          |
| 0.2943        | 0.8243 | 4000 | 0.2613          |
| 0.2787        | 0.8655 | 4200 | 0.2603          |
| 0.2558        | 0.9067 | 4400 | 0.2596          |
| 0.3107        | 0.9480 | 4600 | 0.2593          |
| 0.2894        | 0.9892 | 4800 | 0.2593          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1